knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)
library(here)
library(sf)
library(tmap)
# to update packages use `update.packages(ask = FALSE)`
sf_trees <- read_csv(here("data", "sf_trees", "sf_trees.csv"), show_col_types = FALSE) # wont show the column types
Example 1: Find counts of observations by legal_status & wrangle a bit.
### method 1: `group_by() %>% summarize()
sf_trees %>%
group_by(legal_status) %>%
summarize(tree_count = n())
## # A tibble: 10 × 2
## legal_status tree_count
## <chr> <int>
## 1 DPW Maintained 141725
## 2 Landmark tree 42
## 3 Permitted Site 39732
## 4 Planning Code 138.1 required 971
## 5 Private 163
## 6 Property Tree 316
## 7 Section 143 230
## 8 Significant Tree 1648
## 9 Undocumented 8106
## 10 <NA> 54
### method 2: different way plus a few new functions
top_5_status <- sf_trees %>%
count(legal_status) %>%
drop_na(legal_status) %>%
rename(tree_count = n) %>%
relocate(tree_count, 1) %>% # reorder columns
slice_max(tree_count, n =5) %>% # takes top 5 highest values
arrange(desc(tree_count)) # highest to lowest value sort
Make a graph of the top 5 from above
ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), #fct_reorder orders from smallest to largest # of trees
y = tree_count)) +
geom_col(fill = 'darkgreen') +
labs(x = 'Legal status',
y = 'Tree count') +
coord_flip() + #will flip the axis labels so they fit the entire word
theme_minimal()
Example 2: Only going to keep observations where legal status is “Permitted Site” and caretaker is “MTA”, and store as permitted_data_df
# sf_trees$legal_status %>% unique() # checks for unique values
permitted_data_df <- sf_trees %>%
filter(legal_status == "Permitted Site",
caretaker == "MTA")
Example 3: Only keep Blackwood Acacia trees, and then only keep columns legal_status, date, latitude. and store as blackwood_acacia_df
blackwood_acacia_df <- sf_trees %>%
filter(str_detect(species, 'Blackwood Acacia')) %>%
select(legal_status, date, lat = latitude, lon = longitude)
# Make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
geom_point(color = "darkgreen")
Example 4: use tidyr::separate() to separate words in a column into two separate columns
sf_trees_map <- sf_trees %>%
separate(species, into = c('spp_scientific', 'spp_common'), sep = '::')
Example 5: use tidyr::unite()
ex_5 <- sf_trees %>%
unite('id_status', tree_id, legal_status, sep = ' ADDING THIS ')
Step 1: convert the lat/lon to spatial points, st_as_sf()
blackwood_acacia_sf <- blackwood_acacia_df %>%
drop_na(lat, lon) %>%
st_as_sf(coords = c('lon', 'lat'))
# we need to tell R what the coordinate reference system is
st_crs(blackwood_acacia_sf) <- 4326 #WGS84
ggplot(data = blackwood_acacia_sf) +
geom_sf(color = 'darkgreen') +
theme_minimal()
Read in the SF shapefile and add to map
sf_map <- read_sf(here("data", "sf_map", "tl_2017_06075_roads.shp"))
sf_map_transform <- st_transform(sf_map, 4326)
ggplot(data = sf_map_transform) +
geom_sf()
Combine the maps!
ggplot() +
geom_sf(data= sf_map,
size = 0.1,
color = "darkgrey") + #will be on the bottom layer
geom_sf(data= blackwood_acacia_sf,
size = 0.5,
color = "darkgreen") +
theme_void() +
labs(title = "Blackwood Acacias in SF")
tmap_mode("view")
tm_shape(blackwood_acacia_sf) +
tm_dots()